Scalp EEG brain functional connectivity networks in pediatric epilepsy. (1st January 2015)

• Record Type: Journal Article
• Authors: Sargolzaei, Saman; Cabrerizo, Mercedes; Goryawala, Mohammed; Eddin, Anas Salah; Adjouadi, Malek
• Is Part Of: Computers in biology and medicine. Volume 56(2015)
• Page Start: 158
• Page End: 166
• Publication Date: 2015-01-01
• Subjects: Epilepsy -- Functional connectivity -- Graph theory -- Pediatric -- Scalp EEG; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2014.10.018
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: This study establishes a new data-driven approach to brain functional connectivity networks using scalp EEG recordings for classifying pediatric subjects with epilepsy from pediatric controls. Graph theory is explored on the functional connectivity networks of individuals where three different sets of topological features were defined and extracted for a thorough assessment of the two groups. The rater's opinion on the diagnosis could also be taken into consideration when deploying the general linear model (GLM) for feature selection in order to optimize classification. Results demonstrate the existence of statistically significant ( p <0.05) changes in the functional connectivity of patients with epilepsy compared to those of control subjects. Furthermore, clustering results demonstrate the ability to discriminate pediatric epilepsy patients from control subjects with an initial accuracy of 87.5%, prior to initiating the feature selection process and without taking into consideration the clinical rater's opinion. Otherwise, leave-one-out cross validation (LOOCV) showed a significant increase in the classification accuracy to 96.87% in epilepsy diagnosis. Highlights: Introducing a new data driven graph theory-based methodology for constructing brain functional connectivity networks. Proposing a decision support system for pediatric epilepsy diagnosis. Developing a framework to assess the functional connectivity networks alterations using scalp EEG time series. Evaluation of graph theory measures of brain functional connectivity in pediatric epilepsy diagnosis.